Establishing a Reliable Assessment of the Green View Index Based on Image Classification Techniques, Estimation, and a Hypothesis Testing Route

Sustainable development policies and spatial planning for maintaining greenery are crucial for all major cities in the world, and the measurement of green space indicators in planning practice needs to evolve in response to the demands of the times and technological drivers. This study explores an informal urban green space indicator, the green view index (GVI), which uses the visual perception of an observer to measure the quality of urban space by simulating the pedestrian perspective of the road in street-view image data and then calculating the proportion of vegetation in the road landscape. The GVI is different from macro indicators, such as public recreational green space, forest coverage, and green space rate, which are derived from planning data or remote sensing data in traditional urban planning; it starts from the bottom-up perception of individual residents and is more relevant to their subjective demands. At present, most international cities have made outstanding achievements in controlling public recreational green space, forest coverage, green space rates, and other macrolevel indicators of urban spatial quality; however, with the promotion of the concept of “human-oriented” urban planning, the potential restoration of urban spatial quality at the microlevel is gradually being recognized. To ensure the efficiency and reliability of this study, inspired by computer vision techniques and related GVI studies, a research method based on chromaticity was built to identify the proportions of green vegetation in street view images, and the credibility was improved by eliminating unreliable data. By using this method, we could evaluate a city at an overall scale instead of the previous block scale. The final research result showed that Shenzhen is friendly to human visual senses, and the GVI of the streets in developed areas is generally higher than that in developing areas. The geostatistical analysis of the green viewpoint data provides a more intuitive guide for researchers and planners, and it is believed to inform the planning and design of environmentally friendly, smart, and sustainable future cities.

[1]  Yuan Li,et al.  Visual Preference Analysis and Planning Responses Based on Street View Images: A Case Study of Gulangyu Island, China , 2022, Land.

[2]  Ruoyu Wang,et al.  Visible green space predicts emotion: Evidence from social media and street view data , 2022, Applied Geography.

[3]  W. Halecki,et al.  Assessment and planning of green spaces in urban parks: A review , 2022, Sustainable Cities and Society.

[4]  M. Nyhan,et al.  Associations between Google Street View-Derived Urban Greenspace Metrics and Air Pollution Measured Using a Distributed Sensor Network , 2022, SSRN Electronic Journal.

[5]  Hongxu Wei,et al.  Do Emotional Perceptions of Visible Greeneries Rely on the Largeness of Green Space? A Verification in Nanchang, China , 2022, Forests.

[6]  Samuel Domínguez-Amarillo,et al.  Remote Sensing for the Assessment of Ecosystem Services Provided by Urban Vegetation. A review of the methods applied , 2022, Urban Forestry & Urban Greening.

[7]  Md Sayed Iftekhar,et al.  Valuing the improvement of a decommissioned heritage site to a multifunctional water sensitive greenspace. , 2022, Journal of environmental management.

[8]  D. Vella-Brodrick,et al.  Effects of Nature (Greenspace) on Cognitive Functioning in School Children and Adolescents: a Systematic Review , 2022, Educational Psychology Review.

[9]  Mark A. Goddard,et al.  Can biodiverse streetscapes mitigate the effects of noise and air pollution on human wellbeing? , 2022, Environmental research.

[10]  Zia Khan,et al.  Deep Learning-Based Change Detection in Remote Sensing Images: A Review , 2022, Remote Sensing.

[11]  Ruonan Li,et al.  Integrating Remotely Sensed Leaf Area Index with Biome-BGC to Quantify the Impact of Land Use/Land Cover Change on Water Retention in Beijing , 2022, Remote. Sens..

[12]  F. Slik,et al.  Are street trees friendly to biodiversity? , 2022, Landscape and Urban Planning.

[13]  Aiping Gou,et al.  Numerical Characteristics and Spatial Distribution of Panoramic Street Green View Index Based on SegNet Semantic Segmentation in Savannah , 2022, Urban Forestry & Urban Greening.

[14]  Shizuka Hashimoto,et al.  Residents’ place attachment to urban green spaces in Greater Tokyo region: An empirical assessment of dimensionality and influencing socio-demographic factors , 2022, Urban Forestry & Urban Greening.

[15]  M. D’Amico,et al.  Impacts of roads on bird species richness: A meta-analysis considering road types, habitats and feeding guilds. , 2021, The Science of the total environment.

[16]  F. Biljecki,et al.  Street view imagery in urban analytics and GIS: A review , 2021 .

[17]  Xingyuan He,et al.  Seasonal effects of street trees on particulate matter concentration in an urban street canyon , 2021 .

[18]  D. Barton,et al.  Back to nature: Norwegians sustain increased recreational use of urban green space months after the COVID-19 outbreak , 2021 .

[19]  Hongzan Jiao,et al.  Exploring the Correlation between Block Vitality and Block Environment Based on Multisource Big Data: Taking Wuhan City as an Example , 2021, Land.

[20]  Xiaoqi Feng,et al.  Association between green space, outdoor leisure time and physical activity , 2021, Urban Forestry & Urban Greening.

[21]  Zhiyi Bao,et al.  A study on the impact of Visible Green Index and vegetation structures on brain wave change in residential landscape , 2021 .

[22]  S. Price,et al.  How effective is ‘greening’ of urban areas in reducing human exposure to ground-level ozone concentrations, UV exposure and the ‘urban heat island effect’? An updated systematic review , 2021, Environmental Evidence.

[23]  Linchuan Yang,et al.  To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults , 2021 .

[24]  Nicholas C. Coops,et al.  Mapping trees along urban street networks with deep learning and street-level imagery , 2021 .

[25]  R. Rhodes,et al.  The pathways linking objectively-measured greenspace exposure and mental health: A systematic review of observational studies. , 2021, Environmental research.

[26]  P. Berry,et al.  The climate benefits, co-benefits, and trade-offs of green infrastructure: A systematic literature review. , 2021, Journal of environmental management.

[27]  Pui Kwan Cheung,et al.  Effects of urban park design features on summer air temperature and humidity in compact-city milieu , 2021 .

[28]  John L. Zhou,et al.  A review of strategies for mitigating roadside air pollution in urban street canyons. , 2021, Environmental pollution.

[29]  Samain Sabrin,et al.  Effects of Different Urban-Vegetation Morphology on the Canopy-level Thermal Comfort and the Cooling Benefits of Shade Trees: Case-study in Philadelphia , 2021 .

[30]  Shuomin Zhu,et al.  Comprehensive Evaluation of Healthcare Benefits of Different Forest Types: A Case Study in Shimen National Forest Park, China , 2021, Forests.

[31]  Xiaojiang Li,et al.  Socioeconomic and spatial inequalities of street tree abundance, species diversity, and size structure in New York City , 2021 .

[32]  S. Tsoka,et al.  Assessing the effects of urban street trees on building cooling energy needs: The role of foliage density and planting pattern , 2021 .

[33]  L. Jorge,et al.  A Review on Deep Learning in UAV Remote Sensing , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[34]  P. Antunes,et al.  Green Infrastructure Planning Principles: An Integrated Literature Review , 2020, Land.

[35]  Chuanbin Zhou,et al.  Quantifying the green view indicator for assessing urban greening quality: An analysis based on Internet-crawling street view data , 2020 .

[36]  G. Foody,et al.  Applications in Remote Sensing to Forest Ecology and Management , 2020, One Earth.

[37]  Yeran Sun,et al.  Contextualized effects of Park access and usage on residential satisfaction: A spatial approach , 2020, Land Use Policy.

[38]  Yu Wang,et al.  Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. , 2020, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[39]  Yanlong Zhang,et al.  Stress Recovery of Campus Street Trees as Visual Stimuli on Graduate Students in Autumn , 2019, International journal of environmental research and public health.

[40]  Y. Miyazaki,et al.  Physiological Benefits of Viewing Nature: A Systematic Review of Indoor Experiments , 2019, International journal of environmental research and public health.

[41]  Francisco Rowe,et al.  A Hierarchical Urban Forest Index Using Street-Level Imagery and Deep Learning , 2019, Remote. Sens..

[42]  Perry Hystad,et al.  Evaluating street view exposure measures of visible green space for health research , 2019, Journal of Exposure Science & Environmental Epidemiology.

[43]  Xun Li,et al.  Remote Sensing in Urban Forestry: Recent Applications and Future Directions , 2019, Remote. Sens..

[44]  Yao Yao,et al.  Urban greenery and mental wellbeing in adults: Cross-sectional mediation analyses on multiple pathways across different greenery measures. , 2019, Environmental research.

[45]  Yao Yao,et al.  Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China , 2019, Environment international.

[46]  Bin Chen,et al.  Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. , 2019, Science bulletin.

[47]  Xiangzheng Deng,et al.  Estimates of the social cost of carbon: A review based on meta-analysis , 2019, Journal of Cleaner Production.

[48]  M. Fauvel,et al.  Detection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[49]  J. Byrne,et al.  Factors shaping urban greenspace provision: A systematic review of the literature , 2018, Landscape and Urban Planning.

[50]  Andy P. Jones,et al.  The health benefits of the great outdoors: A systematic review and meta-analysis of greenspace exposure and health outcomes , 2018, Environmental research.

[51]  Christoph D. D. Rupprecht,et al.  Residents’ Perception of Informal Green Space—A Case Study of Ichikawa City, Japan , 2018, Land.

[52]  J. Mennis,et al.  Urban Greenspace is Associated with Reduced Psychological Stress among Adolescents: A Geographic Ecological Momentary Assessment (GEMA) Analysis of Activity Space. , 2018, Landscape and urban planning.

[53]  Carlo Ratti,et al.  Mapping the spatial distribution of shade provision of street trees in Boston using Google Street View panoramas , 2018 .

[54]  Rencai Dong,et al.  Impacts of Street-Visible Greenery on Housing Prices: Evidence from a Hedonic Price Model and a Massive Street View Image Dataset in Beijing , 2018, ISPRS Int. J. Geo Inf..

[55]  Michael W. Strohbach,et al.  Challenges of urban green space management in the face of using inadequate data , 2017 .

[56]  M. Brauer,et al.  Exploring pathways linking greenspace to health: Theoretical and methodological guidance , 2017, Environmental research.

[57]  C. Ratti,et al.  Green streets − Quantifying and mapping urban trees with street-level imagery and computer vision , 2017 .

[58]  Jie Wu,et al.  Use of Tencent Street View Imagery for Visual Perception of Streets , 2017, ISPRS Int. J. Geo Inf..

[59]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[60]  J. Wolch,et al.  Urban green space, public health, and environmental justice: The challenge of making cities ‘just green enough’ , 2014 .

[61]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[62]  K. P. Sharma,et al.  Urban Neighborhood Green Index – A measure of green spaces in urban areas , 2012 .

[63]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[64]  Yoji Aoki.  Evaluation methods for landscapes with greenery , 1991 .

[65]  J. Warland,et al.  Maximizing the pedestrian radiative cooling benefit per street tree , 2023, Landscape and Urban Planning.

[66]  Zilin Wang,et al.  The equity of urban park green space accessibility in large Chinese cities: A case study of Wuhan City , 2022, Progress in Geography.

[67]  Sugie Lee,et al.  Analyzing the effects of Green View Index of neighborhood streets on walking time using Google Street View and deep learning , 2021 .

[68]  Marcus White,et al.  Right tree, right place, right time: A visual-functional design approach to select and place trees for optimal shade benefit to commuting pedestrians , 2020 .

[69]  Alessandro Rigolon,et al.  Inequities in the quality of urban park systems: An environmental justice investigation of cities in the United States , 2018, Landscape and Urban Planning.

[70]  I. Markevych,et al.  Urban residential greenspace and mental health in youth: Different approaches to testing multiple pathways yield different conclusions , 2018, Environmental research.

[71]  Weixing Zhang,et al.  Assessing street-level urban greenery using Google Street View and a modified green view index , 2015 .

[72]  Mohamed H. AlMeer.  Vegetation Extraction from Free Google Earth Images of Deserts Using a Robust BPNN Approach in HSV Space , 2012 .